Matt J. Kusner

Affiliations:
  • University College London, Department of Computer Science, UK
  • Alan Turing Institute, London, UK


According to our database1, Matt J. Kusner authored at least 58 papers between 2013 and 2024.

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Bibliography

2024
Setting the Record Straight on Transformer Oversmoothing.
CoRR, 2024

2023
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DAG Learning on the Permutahedron.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Stochastic Causal Programming for Bounding Treatment Effects.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

Adapting to Latent Subgroup Shifts via Concepts and Proxies.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
MPC-Friendly Commitments for Publicly Verifiable Covert Security.
IACR Cryptol. ePrint Arch., 2022

Causal Machine Learning: A Survey and Open Problems.
CoRR, 2022

Evaluating Self-Supervised Learning for Molecular Graph Embeddings.
CoRR, 2022

Questions for Flat-Minima Optimization of Modern Neural Networks.
CoRR, 2022

Local Latent Space Bayesian Optimization over Structured Inputs.
CoRR, 2022

Causal inference with treatment measurement error: a nonparametric instrumental variable approach.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Local Latent Space Bayesian Optimization over Structured Inputs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

When Do Flat Minima Optimizers Work?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) 2022.
Proceedings of the Algorithmic Fairness through the Lens of Causality and Privacy Workshop, 2022

2021
Graph Intervention Networks for Causal Effect Estimation.
CoRR, 2021

Causal Effect Inference for Structured Treatments.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Counterfactual Data Augmentation for Neural Machine Translation.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Learning Binary Decision Trees by Argmin Differentiation.
Proceedings of the 38th International Conference on Machine Learning, 2021

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction.
Proceedings of the 38th International Conference on Machine Learning, 2021

Operationalizing Complex Causes: A Pragmatic View of Mediation.
Proceedings of the 38th International Conference on Machine Learning, 2021

Unsupervised Point Cloud Pre-training via Occlusion Completion.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Learning Binary Trees via Sparse Relaxation.
CoRR, 2020

Pre-Training by Completing Point Clouds.
CoRR, 2020

A Survey on Contextual Embeddings.
CoRR, 2020

A Class of Algorithms for General Instrumental Variable Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Barking up the right tree: an approach to search over molecule synthesis DAGs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Differentiable Causal Backdoor Discovery.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Cumulo: A Dataset for Learning Cloud Classes.
CoRR, 2019

Gradient Regularized Budgeted Boosting.
CoRR, 2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

A Model to Search for Synthesizable Molecules.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Making Decisions that Reduce Discriminatory Impacts.
Proceedings of the 36th International Conference on Machine Learning, 2019

Generating Molecules via Chemical Reactions.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

A Generative Model For Electron Paths.
Proceedings of the 7th International Conference on Learning Representations, 2019

QUOTIENT: Two-Party Secure Neural Network Training and Prediction.
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

2018
Causal Interventions for Fairness.
CoRR, 2018

Predicting Electron Paths.
CoRR, 2018

Causal Reasoning for Algorithmic Fairness.
CoRR, 2018

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service.
Proceedings of the 35th International Conference on Machine Learning, 2018

Blind Justice: Fairness with Encrypted Sensitive Attributes.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning a Generative Model for Validity in Complex Discrete Structures.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Counterfactual Fairness.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Grammar Variational Autoencoder.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution.
CoRR, 2016

Supervised Word Mover's Distance.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Private Causal Inference.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Deep Manifold Traversal: Changing Labels with Convolutional Features.
CoRR, 2015

Fast Distributed k-Center Clustering with Outliers on Massive Data.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

From Word Embeddings To Document Distances.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Differentially Private Bayesian Optimization.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Classifier cascades and trees for minimizing feature evaluation cost.
J. Mach. Learn. Res., 2014

Stochastic Covariance Compression.
CoRR, 2014

Stochastic Neighbor Compression.
Proceedings of the 31th International Conference on Machine Learning, 2014

Bayesian Optimization with Inequality Constraints.
Proceedings of the 31th International Conference on Machine Learning, 2014

Feature-Cost Sensitive Learning with Submodular Trees of Classifiers.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
Cost-Sensitive Tree of Classifiers.
Proceedings of the 30th International Conference on Machine Learning, 2013

Anytime Representation Learning.
Proceedings of the 30th International Conference on Machine Learning, 2013


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